Neural Network Toolbox

dmsereg

Mean squared error with regularization or performance derivative function

Syntax

dPerf_dE = dmsereg('e',E,X,perf,PP)

dPerf_dX = dmsereg('x',E,X,perf,PP)

Description

dmsereg is the derivative function for msereg.

dmsereg('d',E,X,perf,PP) takes these arguments,

• E -- Matrix or cell array of error vector(s)

  X -- Vector of all weight and bias values

  perf -- Network performance (ignored)

  PP -- mse performance parameter

where PP defines one performance parameters,

• PP.ratio -- Relative importance of errors vs. weight and bias values

and returns the derivative dPerf/dE.

dmsereg('x',E,X,perf) returns the derivative dPerf/dX.

mse has only one performance parameter.

Examples

Here we define an error E and X for a network with one 3-element output and six weight and bias values.

• E = {[1; -2; 0.5]};
  X = [0; 0.2; -2.2; 4.1; 0.1; -0.2];
  

Here the ratio performance parameter is defined so that squared errors are 5 times as important as squared weight and bias values.

• pp.ratio = 5/(5+1);
  

Here we calculate the network's performance, and derivatives of performance.

• perf = msereg(E,X,pp)
  dPerf_dE = dmsereg('e',E,X,perf,pp)
  dPerf_dX = dmsereg('x',E,X,perf,pp)
  

See Also

msereg

dmse

dnetprod

© 1994-2005 The MathWorks, Inc.